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Found 13 papers, 8 papers with code
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex
Using \textit{MacaqueITBench}, we investigated the impact of distribution shifts on models predicting neural activity by dividing the images into Out-Of-Distribution (OOD) train and test splits.
Look Around! Unexpected gains from training on environments in the vicinity of the target
Here we present a new methodology to evaluate such generalization of RL agents under small shifts in the transition probabilities.
Can Machines Imitate Humans? Integrative Turing Tests for Vision and Language Demonstrate a Narrowing Gap
To address this question, we turn to the Turing test and systematically benchmark current AIs in their abilities to imitate humans in three language tasks (Image captioning, Word association, and Conversation) and three vision tasks (Object detection, Color estimation, and Attention prediction).
Improving generalization by mimicking the human visual diet
We present a new perspective on bridging the generalization gap between biological and computer vision -- mimicking the human visual diet.
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available.
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood.
Adversarial examples within the training distribution: A widespread challenge
This result provides evidence supporting theories attributing adversarial examples to the proximity of data to ground-truth class boundaries, and calls into question other theories which do not account for this more stringent definition of adversarial attacks.
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes
Our model captures useful information for contextual reasoning, enabling human-level performance and better robustness in out-of-context conditions compared to baseline models across OCD and other out-of-context datasets.
On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations
We find that learning category and viewpoint in separate networks compared to a shared one leads to an increase in selectivity and invariance, as separate networks are not forced to preserve information about both category and viewpoint.
When and how CNNs generalize to out-of-distribution category-viewpoint combinations
In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization.
Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics
While automatic text extraction works well on infographics, computer vision approaches trained on natural images fail to identify the stand-alone visual elements in infographics, or `icons'.
Understanding Infographics through Textual and Visual Tag Prediction
And second, we use these predicted text tags as a supervisory signal to localize the most diagnostic visual elements from within the infographic i. e. visual hashtags.
Learning Visual Importance for Graphic Designs and Data Visualizations
Our models are neural networks trained on human clicks and importance annotations on hundreds of designs.
